This is the working record behind the four gaps in product management. Each proposal can be proved at small scale first, with an evidence class, a build split and a validation test.
Each gap carries the three fields from the brief, the six-condition test, the evidence class, the build split and a validation test.
A generated requirements document reads equally well whether or not anyone believes in it.
Software licensing names who may use a product, and says nothing of who stands behind a feature.
A software licence extended with an owner and a reason for each feature.
An owner and reason field can be added to each requirement.
The team’s product principles are the reference.
The record can travel with the feature.
Planning tools do not share the field.
Nothing marks features with no owner.
No body checks it.
An owner and reason field on every requirement for one product team.
A shared record across teams and outside partners.
Audit 20 shipped features. If fewer than 70 percent trace to a named owner and a stated reason, the record justifies the build.
A generated summary of customer comments can include content the comments never held.
Rating systems carry customers’ words as a visible score and cannot show what a summary added.
A rating system with a trace from each summary line to the comments behind it.
Each line can link to its source comments, where the tool records them.
The source comments are the reference.
A trace can accompany the summary.
Tools do not share a trace format.
Nothing separates a supported line from an invented one.
No body checks it.
A trace step on one summary type, sampling lines against source comments.
A fidelity score published with each summary.
Sample 100 summary lines against source comments. If more than 10 percent lack support, the trace justifies the build.
A plain-language question to the analytics returns a confident answer built on a definition the asker did not intend.
SLAs promise that an analytics service is available and fast, and say nothing of what a metric means.
A service level that includes a registry of metric definitions.
Each answer can name the definition it used.
A registry of definitions is itself a reference.
Definitions can be shared across tools.
Teams define metrics differently.
Nothing stops an unapproved definition.
No body checks the registry.
A registry for the twenty metrics used most in product reviews.
A shared definition layer across product, finance and marketing.
Ask 30 business questions in plain language. If more than 15 percent of answers use a definition the asker did not intend, the registry justifies the build.
A product that learns after release changes after its launch checklist was signed.
Compliance certification marks that a product met a standard on the day it was tested.
A compliance certification with a post-release learning record: each model change, approver and test.
A change log can record each update.
The EU’s revised product liability rules are a reference.
Insurers could read the record.
No shared format exists.
The EU rules cover software.
National laws are due by 9 December 2026.
A change log for one learning product with each model update, approver and test result.
A record accepted by insurers and regulators.
Review the last ten model updates in one product. If fewer than 80 percent have a complete record, the log justifies the build.
| Gap | Micro-container extended | Value pathway | Who benefits | Payer | First proof to seek |
|---|---|---|---|---|---|
| 01 Requirements with no owner | Software licensing | Cost reduction; faster prioritisation | Product team, engineering | Product operations budget | Named owner on more than 90 percent of features |
| 02 Summaries the customers never wrote | Rating systems | Decision quality; waste avoided | Product team, research | Research operations budget | Unsupported lines below 3 percent |
| 03 Answers asked in plain language | SLAs | Decision quality; analyst time saved | Product team, analytics | Data and analytics budget | Definition mismatch below 5 percent |
| 04 Products that keep learning after release | Compliance certification | Risk reduction; insurance cost | Legal, product, insurer | Product compliance budget | Complete record on every post-release change |
gap_id: Stable identifier, for example MS-PRD-01.
function_id / disruption_ref: Originating function and Disruptions article.
broke: What the abundance broke, in one sentence.
existing_micro_container / economy: The Eras-table micro-container and its economy.
proposed_micro_container: The extension that could hold the gap.
six_conditions: Met, partly met or unmet, with one line of reasoning for each condition.
evidence_class / sources: Evidence label and references.
build_now / build_later: Immediate offering and dependency-based future offering.
validation_test / status: The test to run and the research status.
movers: Leaders Building, Accelerating or Capitalizing, linked to profiles.
Every proposal extends a micro-container the Information Era already built. These are the ones extended here, by economy.
Desktop Economy: Software licensing.
Platform Economy: Rating systems.
Cloud Economy: SLAs, Compliance certification.
Tomorrow is an open canvas. These are the leaders in the record who are already Building, Accelerating or Capitalizing toward these micro-containers.
The micro-containers and pressures this working record builds on.
A controlled trial in which developers with an AI pair programmer finished a defined task 55.8 percent faster.
Models when learning from customer data creates a lasting edge, and when rivals can match it without the data.
Shows how training on model-generated content degrades models, and why data from genuine human interaction gains value.
Tests language models on 632 enterprise data workflows and finds they need significant improvement for real-world use.
Reviews how language generation, including summarisation, produces text unsupported by its source.
Names twelve risks specific to or made worse by generative AI, including information integrity.
Summarises the EU rules that extend product liability to software and AI systems, including products that keep learning.
Funding the build, for readers who can direct capital toward the containerization.
Developing the micro-container that holds the gap, for readers who can make the technology.
Helping the micro-container reach scale, for readers who can set standards, convene consensus or move the political and regulatory conditions.
Note: The perspectives shared on GreenDeveX.com are the author's independent analytical interpretations of trust as a historical medium of exchange. This commentary is intended for conceptual exploration and does not constitute peer-reviewed academic research or commissioned institutional findings.